Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
Product Description
Technology: Deep Learning Tool: MATLAB R2018a
Objective:
The main aim of this project is used to identify the
COVID or healthy from X-ray images using deep learning techniques.
Abstract:
In the rapidly evolving global pandemic of COVID-19, the use of CXR for COVID-19 diagnosis or triage for patient management has become an important issue to preserve limited medical resources and prevent further spreading of the virus. However, the current diagnostic performance with CXR is not sufficient for routine clinical use, so the need of artificial intelligence to improve diagnostic performance of CXR is increasing. Our analysis found that there are statistically significant differences in the patch-wise intensity distribution, which is well-correlated with the radiological findings of the localized intensity variations in COVID-19 CXR. This findings lead us to propose a novel patch-based neural network architecture with random patch cropping, from which the final classification result are obtained by majority voting from inference results at multiple patch locations.
Proposed
System:
The Proposed system focus on
tumor segmentation of medical image sequences using back propagation neural
network. The proposed work utilizes pattern based classification using neural
network function. Active Contour segmentation is designed in the proposed area.
Here the threshold required for segmenting adjusts itself according to the
segmented area and position.
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The Delivery time for Hardware Mini projects is 7-8 working days.